Growing vigilance in the age of AI

Companies are laying off thousands of people, saying that artificial intelligence can take over their jobs. A call for greater vigilance in the age of AI

AI as justification vs. AI as cause

The pretext: In many cases, AI serves as a “good excuse” or catalyst for restructuring that was already planned (e.g. following overstaffing during the pandemic, or to reduce costs). Companies such as Amazon cited “adapting to the AI age” as the reason for staff cuts, even though this was often a classic cost-cutting programme or the rationalisation of overstaffed areas.
The reality: In some industries, however, AI is indeed the direct cause of the collapse of the business model. The US education company Chegg is a prominent example, whose revenues plummeted when ChatGPT offered the same services for free.

The threat to knowledge work

For a long time, jobs requiring higher qualifications were considered secure. However, recent studies (including those by WEF and Microsoft/OpenAI) show that generative AI is fundamentally changing knowledge work.
Professions that involve a lot of text- and data-based routine tasks are particularly affected: editors/proofreaders, legal assistants, accountants, data entry clerks, and even entry-level positions in market research and programming.

The ambivalence of development

Risk of replacement: The WEF predicts that 41% of companies plan to cut jobs due to AI by 2030. Some analysts warn that this could trigger a new wave of offshoring (outsourcing to low-wage countries) if laid-off staff are rehired at lower wages or abroad.
Opportunity for augmentation: Many experts emphasise that AI does not completely replace jobs, but rather transforms them (augmentation). Humans should take on strategic, creative and empathetic tasks, while AI handles routine tasks. Those who learn to use AI tools effectively (keyword: prompt engineering) can increase their performance.
Wrong decisions: There are reports that companies that hastily cut staff in favour of AI later regret this because AI could not completely replace human capabilities and they had to quietly rehire staff.

What does this mean for vigilance?

The call for vigilance is primarily directed at politicians, companies and individuals:

For companies: Don’t just ask, “What can be eliminated?” but “Where do humans demonstrate strengths that AI cannot replace?” and invest in further training for the workforce (e.g. in prompt engineering, data analysis, AI ethics).
For employees: Don’t ignore AI technology, but actively learn and use it to make your own work more efficient. Job losses often threaten those who ignore the possibilities of AI.
For society: Politicians must create rules and guidelines to ensure that the efficiency gains of AI do not only benefit a few, but are also used for social security and further training.

The profession of consultant, and in particular the role of digital/AI transformation manager, is not primarily threatened by AI development, but is undergoing fundamental transformation. These professions are among those that must actively use AI to remain relevant, as their routine tasks are the most vulnerable.

Here is a detailed analysis for these job profiles:

The role of the (strategy) consultant

The work of a consultant consists of various tasks, some of which are strongly influenced by AI, while others actually increase its importance:

Low risk: strategy, empathy and communication (the new core competencies)

The human skills that AI finds difficult to replace are becoming crucial for consultants:

Strategic consulting: Developing tailor-made, company-specific strategies that go beyond mere data analysis. Interpreting complex results and deriving sustainable visions remain human domains.
Stakeholder management & change: Convincing executives, managing resistance, leading workshops and providing emotional support during transformation processes require empathy and interpersonal intelligence – key areas in which AI (still) has its limitations.
Acquisition and trust building: The ability to establish new customer relationships and build long-term trust.

High risk: Routine and data evaluation (augmentation and automation)

AI will significantly change or replace the lower and middle ranks of consulting, especially at the junior level:

Data collection and analysis: AI can collect, cleanse and evaluate large amounts of data for standard reports or market analyses in seconds.
Creation of standard presentations: Drafting initial slides, summarising interviews or formatting documents will be largely automated.
Market and competition research: Routine research tasks that used to take hours are now massively accelerated by intelligent search and summarisation tools.

Conclusion for consultants: The job is not being replaced, but rather “upgraded.” Consultants who master prompt engineering and use AI as a powerful tool to increase efficiency can handle more complex projects in less time. However, those who limit themselves to routine work risk being left behind by AI-augmented competition.

The role of the digital/AI transformation manager

This occupational group is essentially the labour market’s response to the AI revolution.

📈 High demand and low risk

Digital and AI transformation managers are currently and will continue to be in high demand in the long term, as they are the experts who bring about and steer change in companies. Their tasks are, by definition, future-oriented:

Strategic design of change: They draw up the roadmap for how AI is to be integrated into the company.
Project management and implementation: They translate the technology into concrete business processes and manage teams and budgets.
Ethics and governance: They ensure that AI systems are used fairly, transparently and in compliance with the law – a crucial task with increasing complexity.
Mediation and training: They act as an interface between the tech department and specialist departments and are responsible for change management and staff training.

Conclusion for transformation managers: They are the architects of the AI era in the company. Although they need to stay up to date with technology, their primary skills (leadership, organisation, communication and ethical evaluation) are the least automatable.

Appeal: Vigilance as an opportunity

For consultants and transformation managers, the call for vigilance means one thing above all else: building competence.

Focus	Previously (routine)	Today/tomorrow (AI-augmented)
Consultants	Transferring data to PowerPoint	Strategic interpretation of AI results
Transformation manager	Simple software implementation	Shaping AI governance and culture

Those who ignore AI risk a drastic decline in efficiency. Those who master it will become indispensable partners and drivers of change.

Reasons why this has not yet taken hold in Germany

This is the key question currently being hotly debated in German politics and business. The discrepancy you describe – international companies laying off staff in favour of AI, while many German companies are still hesitant – is a visible sign of what is often referred to as the “German AI paradox”.

Although Germany is often at the forefront of AI research and large corporations are actively investing, the widespread application of AI, especially in small and medium-sized enterprises, is lagging behind.

Here are the main reasons why the AI transformation has not yet fully arrived in many German companies, despite its well-known potential:

🇩🇪 The top 4 reasons for hesitation

Data infrastructure and quality (the fundamental flaw)

AI systems are only as good as the data they are trained with. In many British companies, especially established SMEs, there are massive deficits:

Data silos: Data is distributed across outdated, unconnected systems (fragmented infrastructure).
Poor quality: Data is often incomplete, inconsistent or not structured for machine learning.
Lack of standards: Before AI can automate processes, complex data cleansing and structuring often has to be carried out first, which takes time and incurs high costs.

Shortage of skilled workers and lack of knowledge (the human capital gap)

The problem lies not only in the technology, but also in people’s minds:

Lack of expertise: There is a shortage of specialised personnel (data scientists, AI ethicists, machine learning engineers) to develop and implement complex AI projects.
Knowledge gap among managers: Many decision-makers still have too little knowledge about the concrete applicability of AI. This leads to unrealistic expectations or, on the contrary, to exaggerated scepticism and reluctance to approve budgets.
Resistance and fear: Employees often fear losing their jobs, which slows down the acceptance of new AI tools (change management becomes a challenge).

Bureaucracy, regulation and data protection (the “German Angst” factor)

Fear of compliance violations is traditionally high in Germany and is exacerbated by new legislation:

EU AI Act & GDPR: The strict EU AI Act and the high requirements of the GDPR (General Data Protection Regulation) create uncertainty. Companies fear that using AI could lead to legal conflicts, especially when training with personal data.
“Wait and see”: Many companies are adopting a wait-and-see attitude (“What experiences are others having?”) instead of taking active measures.

Investment focus and corporate culture (the short-term trap)

German corporate culture is often geared towards short-term, measurable success (ROI) and tends towards perfection before a product is rolled out:

Focus on efficiency, not growth: Many AI projects are primarily aimed at reducing costs and increasing the efficiency of existing processes, rather than creating disruptive new business models. The huge, high-risk potential for innovation remains untapped.
SMEs as the backbone: German SMEs, the backbone of the economy, are stable but often shy away from high, long-term initial investments, as the amortisation of AI projects can be complex and lengthy.

Conclusion: The race has only just begun

The hesitancy of German companies is rarely due to a lack of recognition that AI is important (most see AI as crucial to competitiveness). Rather, it is due to structural hurdles such as poor data, regulatory fears and a lack of experts who can actively drive change.